Experimental estimates and expected frequency
Estimate probabilities from recorded trials, pool unequal batches correctly and interpret expected frequency as a model prediction that may be fractional.
Exam scope and assessment notes
The first five lessons are shared Foundation work inherited by Higher; the final four are additional Higher trees, simple conditioning and applications. State the actual random mechanism, equally likely elementary outcomes, complete sample spaces and event membership. Distinguish independence from replacement and mutual exclusivity, observed relative frequency from theoretical probability, and expected frequency from a guaranteed count. Retain every ordered without-replacement history and its remaining contents. Conditional probabilities use the conditioning population; an empty condition is undefined. Inverse object counts are nonnegative integers in the original positive-denominator model. Written reasoning and assumptions still need human review; no marks or mastery are awarded. Specialist and formal-assessment approval remain pending. This is Issue2 linear4MA1; November2026/June2027 and regional-centre eligibility differ from modular awards. Literal Book1 HandlingData1/2/3 and Hub tags are navigation only; no Book2 tag or probability chapter/Hub body is verified. Keep map228–242/proof229–243/next244, map303/proof304–328/next329 and map398–428/proof397/summary426/next428 separate. Missing explicit Hub tags A/B/F/J stay missing. The common elementary two-coin model is not claimed as a novel model. Statistical-measures and diagram prerequisites do not themselves establish probability coverage.
Before you begin
- proportions and counts
- PR1
- multiplication with fractions
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
A model gives a noninteger expected number of events in a future collection. What does that mean?
What you will learn
- Estimate probability by observed event frequency divided by completed trials; distinguish an estimate from a theoretical model.
- Predict expected frequency as trial count × stated/estimated probability, including fractional expected counts.
- Pool raw counts rather than average unequal batch proportions; explain comparable conditions, non-guarantee, sample bias and missing totals.
Of 80 recorded serves, 52 landed in. Estimate the probability a comparable serve lands in and the expected number landing in among 200 future serves. Explain why the result is not a guarantee.
Enter the original physical identities, ordered outcomes, selected populations, weights, observed counts and actual geometry yourself. No space, history, support, denominator, rate, count, unknown proposal or probability is filled. A recorded representation is an attempt, not a correctness result.
Original givens and my literal probability work
Original supplied information
observedTotal
80observedEvents
52futureTrials
200context
successful servesThe original physical labels, given weights and stated mechanism are supplied. No completed sample space, tree, event selection, total, relative frequency, expected count, unknown count or requested probability is filled. Counts determine masses; drawn circle areas and tree lengths do not.
Every planar panel shares this uniform camera. Experiment rate positions are categorical layout positions, not a fabricated cumulative trial history. The prediction panel labels its separate trials/expected-events units. Pan, zoom and turn affect the view only.
Empty own drawing plane; no probability representation is supplied
Blue supplied diagrams are schematic; amber records are actual learner work; purple is dirty retained work; dashed work is unready. Native clipping does not clamp, snap or repair points. Exact keyboard tokens and numeric-only free inverse coordinates are separate: a finite inverse click remains actual raw work even when exact-token bounds later reject it. The finite tool uses coordinates±1,000,000, up to40 tree nodes/81 terminals/4 branches,100 grid cells or weights. These are tool limits. No numerical agreement, written-text presence, drawn tree, sample space or reference proves reasoning, grants marks/mastery, or supplies teacher/specialist/formal-assessment or book-body approval.
Plot my observed rates and expected-frequency model
No actual representation is recorded.
The question’s event names are not an additional event-selection task for this representation. Enter an event row and its actual favourable/conditioning populations yourself. The original replacement or independence mechanism remains the supplied question; the interface never generates remaining counts or conditional weights.
My quantities, conclusions and explanation
Keep the original event support, probability/conditioning assumptions and requested format. No denominator or answer is inserted.
Keep the original event support, probability/conditioning assumptions and requested format. No denominator or answer is inserted.
A nonblank explanation is an attempt for human review, not approval of reasoning.
These original finite cases are self-checks, not automatic marking, mastery, teacher/specialist/formal-assessment or unavailable book-body approval. Equally likely physical outcomes, independence, replacement and comparable experimental conditions must be justified from each original question. An empty conditioning population is undefined, not probability zero. Expected counts are model predictions, not guaranteed observations. No statistics or modular credit is inferred.
Separate observation from a model
Previously collected data can suggest a probability through relative frequency: observed event count divided by completed trial count. Both counts refer to the same stated collection of trials. Check that the total is positive and that the event count is a whole number from zero up to that total. No observed-trial ratio is defined when there were no trials.
An experimental estimate and a theoretical model answer different questions. The estimate summarises what happened in a particular sample; the theoretical probability follows from stated assumptions about the process. A finite observed proportion may differ from its model probability through random variation, changing conditions or a poor sample. Numerical disagreement alone is not a logical proof that the theoretical model is false.
Use expected frequency with its assumptions
For a stated or estimated event probability p, the expected frequency in N comparable trials is N multiplied by p. State what event is being predicted and which probability is used. Comparable conditions matter: an estimate from a different population, altered process or biased sample may not support the new prediction even when the multiplication is accurate.
An expected frequency describes a model average or prediction, not a guaranteed realised count. The product can be fractional even though a single observed number of people, departures or faulty items must be an integer. Do not round the probability early to force an integer result, and do not interpret a fractional expectation as a fraction of an actual individual.
Pool counts and interpret variability
When comparable batches have different sizes, pool their event counts and trial totals before dividing. The larger batch contributes more observations and therefore more weight. An unweighted mean of batch proportions generally gives the wrong pooled proportion unless the batch sizes are equal or a special numerical coincidence occurs. Keep the original counts available so that the weighting can be checked.
A larger sample can reduce ordinary random fluctuation under a stable representative process, but it does not automatically remove bias or repair changed conditions. Give a relevant limitation rather than promising an exact future count. This work estimates probabilities and expected counts; it does not perform a formal statistical test or establish that a learner has validated a real-world mechanism.
Sources for this lesson
- Pearson Mathematics A linear specification · Issue2 · Foundation printed24–25/physical30–31; Higher printed39/physical45; inheritance printed27/physical33
- Pearson2026/2027 entry manual · centre regions physical3; Mathematics A physical25
- Pearson second-edition modular book/Hub map · literal navigation only · physical4,6,7
Pause and explain
A comparable sample has 7 events in 50 trials. What is the expected event count in 80 future trials using this estimate?
Worked example
Of 80 recorded serves, 52 landed in. Estimate the probability a comparable serve lands in and the expected number landing in among 200 future serves. Explain why the result is not a guarantee.
Show the worked solution
- Write the original observed event and completed-trial counts separately. Form the relative frequency, then multiply by the stated future trial count under comparable conditions; keep observed and expected quantities distinct.
- The relative frequency is 52/80=13/20=0.65, using all 80 completed trials.
- For 200 comparable future trials, expected frequency=200×13/20=130.
- 130 is a prediction under a stable comparable process, not a promise. The player, conditions or recording method could change, and finite random trials vary.
Answer Estimated probability 52/80=13/20=0.65; expected frequency 200×13/20=130 comparable serves. The count 130 is a model prediction, not a guaranteed realised result.
Try a topic test
Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.
Test this topic →From guided practice to a new situation
Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
A sample of 150 bulbs contains 9 faulty bulbs. Estimate the faulty probability and the expected number of faulty bulbs in 400 comparable bulbs. State one reason the sample might give a poor prediction.
Give me a hint
Separate the observed event/total ratio from the future trial count, and keep the original counts when combining batches.
Compare my reasoning
- Write the original observed event and completed-trial counts separately. Form the relative frequency, then multiply by the stated future trial count under comparable conditions; keep observed and expected quantities distinct.
- The sample relative frequency is 9/150=3/50=0.06.
- Expected faulty count in 400 is 400×3/50=24.
- A sample selected from an unusual batch, or a changed production process, may not represent the future group. A larger trial count does not by itself repair biased selection.
Estimated faulty probability 9/150=3/50=0.06; expected faulty count 400×3/50=24. Biased sample selection or changed manufacturing conditions could make the sample a poor guide.
Look for these in your work
- Use 150 as denominator.
- Give an expected count of 24.
- Name a relevant representativeness limitation.
A log records 84 on-time, 30 late and 6 cancelled departures. Estimate each category probability and the expected number cancelled among 250 comparable departures. Explain whether a fractional expected count is meaningful.
Give me a hint
Separate the observed event/total ratio from the future trial count, and keep the original counts when combining batches.
Compare my reasoning
- Write the original observed event and completed-trial counts separately. Form the relative frequency, then multiply by the stated future trial count under comparable conditions; keep observed and expected quantities distinct.
- The categories total 120. Their relative frequencies are 84/120=7/10, 30/120=1/4 and 6/120=1/20.
- The expected cancellation count is 250×1/20=12.5.
- 12.5 is an expected frequency, not a possible realised number of departures. Actual cancellation counts are integers and need not equal this model-based average prediction.
Total 120; estimated probabilities on time 7/10, late 1/4, cancelled 1/20. Expected cancellations 250×1/20=25/2=12.5. A fractional expectation is meaningful as a model average, while a realised cancellation count is an integer.
Look for these in your work
- Check the exhaustive category total.
- Use the cancellation estimate.
- Distinguish expected count from actual integer count.
Two comparable batches record 18 successes out of 40 and 39 out of 60. Pool the trials to estimate the success probability and expected successes in 250 future trials. Explain why the unweighted mean of the two batch proportions is wrong, and whether the pooled result proves a stated theoretical probability 3/5 false.
Give me a hint
Separate the observed event/total ratio from the future trial count, and keep the original counts when combining batches.
Compare my reasoning
- Write the original observed event and completed-trial counts separately. Form the relative frequency, then multiply by the stated future trial count under comparable conditions; keep observed and expected quantities distinct.
- Pool successes 18+39=57 and trials 40+60=100, giving 57/100. The groups have different sizes.
- Expected frequency in 250 is 250×57/100=142.5. Averaging 18/40 and 39/60 equally gives 0.55 rather than the correct pooled 0.57.
- The theoretical model 3/5 predicts 60 successes in 100 as an expected count, not an exact count. Observing 57 does not by itself logically prove the model false; process and sampling assumptions still matter.
Pooled successes 57 in 100 trials give 57/100; expected successes in 250 are 285/2=142.5. The unweighted mean is 11/20=0.55 and gives unequal batches the wrong weights. The stated theoretical 3/5 predicts 60 successes in 100 as an expectation; observing 57 does not by itself prove that model false.
Look for these in your work
- Pool event counts and totals.
- Explain unequal weights and fractional expectation.
- Distinguish observed deviation from proof of a false model.
Common mistakes
- Dividing by a future trial count instead of the original observed total.
- Averaging unequal batch proportions with equal weights.
- Calling a fractional expected frequency impossible or a prediction guaranteed.
What can you explain now?
Why can pooling raw counts be better than taking an unweighted mean of two sample proportions?
Compare with the explanation
Pooling weights each observation equally, whereas an unweighted mean gives batches equal influence despite different sizes.
Add event counts and completed trial totals separately, then divide; retain comparable-process and representativeness assumptions.
After trying it yourself, choose your next review. This is your self-assessment.
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Make it stick
Explain the original estimate, pooled weighting, expected-frequency multiplication and a relevant limitation of the prediction.
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.